-
Context Engineering
What is Context Engineering? Context Engineering is the deliberate design and orchestration of all information sent into a language model’s context window—not just the prompt, but also memory, retrieved knowledge, prior outputs, tool descriptions, and more. It aims to ensure the model has everything it needs at inference time to perform optimally. In short, it’s…
-
How to choose right Language model
Generative AI (GenAI) and large language models (LLMs) are transforming how businesses operate, automate tasks, and make decisions. If you’re looking to integrate GenAI into your organization, choosing the right language model is crucial. Here’s a comprehensive framework to help you decide: 1. Define Your Use Case Start by clearly understanding your specific needs: 2.…
-
Google’s Project Mariner
What is Project Mariner? Project Mariner is a research prototype that explores the future of human-agent interaction, particularly within web browsers. It automates tasks such as online shopping, information retrieval, and form-filling, aiming to enhance user productivity by delegating routine web-based tasks to an AI agent. https://deepmind.google/models/project-mariner/ ⚙️ Key Features 🧪 Real-World Applications 🧑💻 Developer…
-
Model Context Protocol (MCP)
What is MCP? MCP (Model Context Protocol) is an open standard that defines how AI applications, especially those using LLMs (Large Language Models), connect to external data and tools. Simple Analogy: Without MCP:You’re speaking a different “language” in every country you visit. With MCP:Everyone speaks the same language — so you can navigate anywhere with…
-
AI Agent – Part4
In Part 3, we explored an example of developing an AI agent using the LangChain framework. While LangChain is powerful for building straightforward agent applications, there are several other frameworks available that cater to a wide range of agentic application needs. In this post, we will explore some of these leading frameworks, highlighting their unique…
-
AI Agent – Part 3
In Part 3 of our series, we take the concepts explored in Part 2 and bring them to life with code. We’ll build a customer service agent that dynamically selects the appropriate tool based on customer queries. The agent will use LangChain and OpenAI to perform actions like checking order status and initiating returns. Setting…
-
AI Agent – Part 2
In Part 2, we focus on how Large Language Models (LLMs), like OpenAI’s GPT models, can plan and use external tools to perform specific tasks in customer service. The core concept here is that the AI agent doesn’t simply respond to a query but intelligently plans which tool to invoke based on the customer’s input.…
-
AI Agent – Part 1
Q: What are AI agents? AI agents are autonomous software programs designed to perform tasks on behalf of users or other systems without human intervention. They utilize artificial intelligence techniques, such as machine learning and natural language processing, to understand their environment, make decisions, and execute actions to achieve specific goals. Q: How do AI…
-
US Copyright Office Outlines Copyright Rules for AI-Generated Content
Top 10 Key Points on AI and Copyright from the U.S. Copyright Office Report These key takeaways provide valuable insights into the evolving relationship between copyright law and generative AI, highlighting the importance of human creativity and ethical AI practices. Reference – https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-2-Copyrightability-Report.pdf
-
LLM/GenAI’s Learning Materials
A curated collection of resources designed to help you deepen your understanding of Large Language Models (LLMs) and Generative AI (GenAI). Whether you’re just starting your journey in AI or looking to expand your expertise, this page provides valuable learning materials from various trusted sources. These resources will guide you through the foundational concepts, cutting-edge…
-
Subscribe
Subscribed
Already have a WordPress.com account? Log in now.
Vijaymathialagan.ai 